基于最小绝对收缩与选择算子模型稀疏恢复的多目标检测

Multi-target detection via sparse recovery of least absolute shrinkage and selection operator model

针对地面多径环境下运动目标检测,使用最小绝对收缩与选择算子(LASSO)算法在参数估计时会出现伪目标的问题,提出一种基于LASSO模型框架的设计矩阵降维构造方法。首先,信号的多径传播能够带来目标检测的空间分集,信号在不同的多径上有不同的多普勒频移;此外,使用宽带正交频分复用(OFDM)信号能够带来频率分集。由于空间分集和频率分集的引入造成目标的稀疏特性。利用多径的稀疏性和对环境的先验知识,去估计稀疏向量。仿真结果表明,在一定信噪比(SNR,-5 d B)下,基于设计矩阵降维构造方法的改进的LASSO算法比基追踪算法(BP)、DS(Dantzig Selector)、LASSO等传统算法的检测性能有明显提高;在一定虚警率(0.1)条件下,改进的LASSO算法比原LASSO算法检测概率提高了30%。所提算法能够有效去除伪目标,提高雷达目标检测概率。

Focusing on the issue that the Least Absolute Shrinkage and Selection Operator（ LASSO） algorithm may introduce some false targets in moving target detection with the presence of multipath reflections, a descending dimension method for designed matrix based on LASSO was proposed. Firstly, the multipath propagation increases the spatial diversity and provides different Doppler shifts over different paths. In addition, the application of broadband OFDM signal provides frequency diversity. The introduction of spatial diversity and frequency diversity to the system causes target space sparseness.Sparseness of multiple paths and environment knowledge were applied to estimate paths along the receiving target responses.Simulation results show that the improved LASSO algorithm based on the descending dimension method for designed matrix has better detection performance than the traditional algorithms such as Basis Pursuit（ BP）, Dantzig Selector（ DS） and LASSO at the Signal-to-Noise Ratio（ SNR） of-5 d B, and the target detection probability of the improved LASSO algorithm was 30%higher than that of LASSO at the false alarm rate of 0. 1. The proposed algorithm can effectively filter the false targets and improve the radar target detection probability.